Overview

Dataset statistics

Number of variables15
Number of observations614
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory34.8 KiB
Average record size in memory58.0 B

Variable types

Numeric3
Categorical12

Alerts

Loan_Amount_Term has constant value ""Constant
Dataset has 1 (0.2%) duplicate rowsDuplicates
ApplicantIncome is highly overall correlated with LoanAmountHigh correlation
LoanAmount is highly overall correlated with ApplicantIncomeHigh correlation
Credit_History is highly overall correlated with Loan_Status_YHigh correlation
Property_Area_Semiurban is highly overall correlated with Property_Area_UrbanHigh correlation
Property_Area_Urban is highly overall correlated with Property_Area_SemiurbanHigh correlation
Loan_Status_Y is highly overall correlated with Credit_HistoryHigh correlation
Dependents_3+ is highly imbalanced (58.7%)Imbalance
CoapplicantIncome has 273 (44.5%) zerosZeros

Reproduction

Analysis started2023-07-10 12:11:32.885340
Analysis finished2023-07-10 12:11:58.265444
Duration25.38 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

ApplicantIncome
Real number (ℝ)

Distinct458
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4617.1116
Minimum150
Maximum10171.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-07-10T17:41:59.185265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile1897.55
Q12877.5
median3812.5
Q35795
95-th percentile10171.25
Maximum10171.25
Range10021.25
Interquartile range (IQR)2917.5

Descriptive statistics

Standard deviation2479.8517
Coefficient of variation (CV)0.53710024
Kurtosis0.1264778
Mean4617.1116
Median Absolute Deviation (MAD)1229.5
Skewness1.039846
Sum2834906.5
Variance6149664.6
MonotonicityNot monotonic
2023-07-10T17:41:59.935215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10171.25 50
 
8.1%
2500 9
 
1.5%
4583 6
 
1.0%
6000 6
 
1.0%
2600 6
 
1.0%
4166 5
 
0.8%
3333 5
 
0.8%
5000 5
 
0.8%
3750 5
 
0.8%
8333 4
 
0.7%
Other values (448) 513
83.6%
ValueCountFrequency (%)
150 1
0.2%
210 1
0.2%
416 1
0.2%
645 1
0.2%
674 1
0.2%
1000 1
0.2%
1025 2
0.3%
1299 1
0.2%
1378 1
0.2%
1442 1
0.2%
ValueCountFrequency (%)
10171.25 50
8.1%
10139 1
 
0.2%
10047 1
 
0.2%
10000 3
 
0.5%
9963 1
 
0.2%
9833 1
 
0.2%
9703 1
 
0.2%
9560 1
 
0.2%
9538 1
 
0.2%
9508 1
 
0.2%

CoapplicantIncome
Real number (ℝ)

Distinct271
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1419.7022
Minimum0
Maximum5743.125
Zeros273
Zeros (%)44.5%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-07-10T17:42:01.155107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1188.5
Q32297.25
95-th percentile4997.4
Maximum5743.125
Range5743.125
Interquartile range (IQR)2297.25

Descriptive statistics

Standard deviation1624.6059
Coefficient of variation (CV)1.1443286
Kurtosis0.24471109
Mean1419.7022
Median Absolute Deviation (MAD)1188.5
Skewness1.0127628
Sum871697.17
Variance2639344.3
MonotonicityNot monotonic
2023-07-10T17:42:02.525835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 273
44.5%
5743.125 18
 
2.9%
1666 5
 
0.8%
2500 5
 
0.8%
2083 5
 
0.8%
1750 3
 
0.5%
2333 3
 
0.5%
1625 3
 
0.5%
5625 3
 
0.5%
1800 3
 
0.5%
Other values (261) 293
47.7%
ValueCountFrequency (%)
0 273
44.5%
16.12000084 1
 
0.2%
189 1
 
0.2%
240 1
 
0.2%
242 1
 
0.2%
461 1
 
0.2%
484 1
 
0.2%
505 1
 
0.2%
536 1
 
0.2%
663 1
 
0.2%
ValueCountFrequency (%)
5743.125 18
2.9%
5701 1
 
0.2%
5654 1
 
0.2%
5625 3
 
0.5%
5624 1
 
0.2%
5500 1
 
0.2%
5302 1
 
0.2%
5296 1
 
0.2%
5266 1
 
0.2%
5064 1
 
0.2%

LoanAmount
Real number (ℝ)

Distinct169
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.02535
Minimum9
Maximum261.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2023-07-10T17:42:04.157831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile57.3
Q1100.25
median129
Q3164.75
95-th percentile261.5
Maximum261.5
Range252.5
Interquartile range (IQR)64.5

Descriptive statistics

Standard deviation55.773951
Coefficient of variation (CV)0.40408482
Kurtosis0.082052061
Mean138.02535
Median Absolute Deviation (MAD)30.5
Skewness0.64862708
Sum84747.568
Variance3110.7336
MonotonicityNot monotonic
2023-07-10T17:42:05.070385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
261.5 41
 
6.7%
146.4121622 22
 
3.6%
120 20
 
3.3%
110 17
 
2.8%
100 15
 
2.4%
187 12
 
2.0%
160 12
 
2.0%
128 11
 
1.8%
113 11
 
1.8%
130 10
 
1.6%
Other values (159) 443
72.1%
ValueCountFrequency (%)
9 1
0.2%
17 1
0.2%
25 2
0.3%
26 1
0.2%
30 2
0.3%
35 1
0.2%
36 1
0.2%
40 2
0.3%
42 1
0.2%
44 2
0.3%
ValueCountFrequency (%)
261.5 41
6.7%
260 3
 
0.5%
259 2
 
0.3%
258 2
 
0.3%
255 3
 
0.5%
253 1
 
0.2%
250 1
 
0.2%
246 1
 
0.2%
244 1
 
0.2%
243 1
 
0.2%

Loan_Amount_Term
Categorical

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
360.0
614 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3070
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row360.0
2nd row360.0
3rd row360.0
4th row360.0
5th row360.0

Common Values

ValueCountFrequency (%)
360.0 614
100.0%

Length

2023-07-10T17:42:05.825042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:06.720479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
360.0 614
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1228
40.0%
3 614
20.0%
6 614
20.0%
. 614
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2456
80.0%
Other Punctuation 614
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1228
50.0%
3 614
25.0%
6 614
25.0%
Other Punctuation
ValueCountFrequency (%)
. 614
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1228
40.0%
3 614
20.0%
6 614
20.0%
. 614
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1228
40.0%
3 614
20.0%
6 614
20.0%
. 614
20.0%

Credit_History
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size40.8 KiB
1.0
525 
0.0
89 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1842
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 525
85.5%
0.0 89
 
14.5%

Length

2023-07-10T17:42:07.440095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:08.160167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 525
85.5%
0.0 89
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 703
38.2%
. 614
33.3%
1 525
28.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1228
66.7%
Other Punctuation 614
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 703
57.2%
1 525
42.8%
Other Punctuation
ValueCountFrequency (%)
. 614
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 703
38.2%
. 614
33.3%
1 525
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 703
38.2%
. 614
33.3%
1 525
28.5%

Gender_Male
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
1
502 
0
112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 502
81.8%
0 112
 
18.2%

Length

2023-07-10T17:42:08.680306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:09.200195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 502
81.8%
0 112
 
18.2%

Most occurring characters

ValueCountFrequency (%)
1 502
81.8%
0 112
 
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 502
81.8%
0 112
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 502
81.8%
0 112
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 502
81.8%
0 112
 
18.2%

Married_Yes
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
1
401 
0
213 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 401
65.3%
0 213
34.7%

Length

2023-07-10T17:42:09.675348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:10.215075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 401
65.3%
0 213
34.7%

Most occurring characters

ValueCountFrequency (%)
1 401
65.3%
0 213
34.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 401
65.3%
0 213
34.7%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 401
65.3%
0 213
34.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 401
65.3%
0 213
34.7%

Dependents_1
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
512 
1
102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 512
83.4%
1 102
 
16.6%

Length

2023-07-10T17:42:10.810306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:11.485103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 512
83.4%
1 102
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 512
83.4%
1 102
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 512
83.4%
1 102
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 512
83.4%
1 102
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 512
83.4%
1 102
 
16.6%

Dependents_2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
513 
1
101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 513
83.6%
1 101
 
16.4%

Length

2023-07-10T17:42:11.955052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:12.625241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 513
83.6%
1 101
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 513
83.6%
1 101
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 513
83.6%
1 101
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 513
83.6%
1 101
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 513
83.6%
1 101
 
16.4%

Dependents_3+
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
563 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 563
91.7%
1 51
 
8.3%

Length

2023-07-10T17:42:13.050314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:13.625467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 563
91.7%
1 51
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 563
91.7%
1 51
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 563
91.7%
1 51
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 563
91.7%
1 51
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 563
91.7%
1 51
 
8.3%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
480 
1
134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 480
78.2%
1 134
 
21.8%

Length

2023-07-10T17:42:14.030408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:14.465369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 480
78.2%
1 134
 
21.8%

Most occurring characters

ValueCountFrequency (%)
0 480
78.2%
1 134
 
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 480
78.2%
1 134
 
21.8%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 480
78.2%
1 134
 
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 480
78.2%
1 134
 
21.8%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
532 
1
82 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 532
86.6%
1 82
 
13.4%

Length

2023-07-10T17:42:14.895417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:15.404715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 532
86.6%
1 82
 
13.4%

Most occurring characters

ValueCountFrequency (%)
0 532
86.6%
1 82
 
13.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 532
86.6%
1 82
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 532
86.6%
1 82
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 532
86.6%
1 82
 
13.4%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
381 
1
233 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 381
62.1%
1 233
37.9%

Length

2023-07-10T17:42:15.895153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:16.420157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 381
62.1%
1 233
37.9%

Most occurring characters

ValueCountFrequency (%)
0 381
62.1%
1 233
37.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 381
62.1%
1 233
37.9%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 381
62.1%
1 233
37.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 381
62.1%
1 233
37.9%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
412 
1
202 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 412
67.1%
1 202
32.9%

Length

2023-07-10T17:42:16.895361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:17.430008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 412
67.1%
1 202
32.9%

Most occurring characters

ValueCountFrequency (%)
0 412
67.1%
1 202
32.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 412
67.1%
1 202
32.9%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 412
67.1%
1 202
32.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 412
67.1%
1 202
32.9%

Loan_Status_Y
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
1
422 
0
192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 422
68.7%
0 192
31.3%

Length

2023-07-10T17:42:17.880068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-10T17:42:18.405086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 422
68.7%
0 192
31.3%

Most occurring characters

ValueCountFrequency (%)
1 422
68.7%
0 192
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 422
68.7%
0 192
31.3%

Most occurring scripts

ValueCountFrequency (%)
Common 614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 422
68.7%
0 192
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 422
68.7%
0 192
31.3%

Correlations

2023-07-10T17:42:18.890343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ApplicantIncomeCoapplicantIncomeLoanAmountCredit_HistoryGender_MaleMarried_YesDependents_1Dependents_2Dependents_3+Education_Not GraduateSelf_Employed_YesProperty_Area_SemiurbanProperty_Area_UrbanLoan_Status_Y
ApplicantIncome1.000-0.3200.5010.0000.1060.0000.0000.0000.0000.1850.2650.0590.0730.000
CoapplicantIncome-0.3201.0000.2320.0000.2040.3120.0000.1250.0000.1530.1150.0000.0550.053
LoanAmount0.5010.2321.0000.0000.1530.1840.0550.0610.1260.1770.0980.0000.1070.097
Credit_History0.0000.0000.0001.0000.0000.0000.0000.0000.0330.0550.0000.0000.0000.534
Gender_Male0.1060.2040.1530.0001.0000.3580.0000.1180.0790.0000.0000.0960.0000.000
Married_Yes0.0000.3120.1840.0000.3581.0000.1020.2420.1200.0000.0000.0000.0000.078
Dependents_10.0000.0000.0550.0000.0000.1021.0000.1880.1200.0000.0640.0000.0510.000
Dependents_20.0000.1250.0610.0000.1180.2420.1881.0000.1190.0000.0000.0000.0000.041
Dependents_3+0.0000.0000.1260.0330.0790.1200.1200.1191.0000.0260.0000.0000.0080.000
Education_Not Graduate0.1850.1530.1770.0550.0000.0000.0000.0000.0261.0000.0000.0000.0000.071
Self_Employed_Yes0.2650.1150.0980.0000.0000.0000.0640.0000.0000.0001.0000.0000.0000.000
Property_Area_Semiurban0.0590.0000.0000.0000.0960.0000.0000.0000.0000.0000.0001.0000.5430.127
Property_Area_Urban0.0730.0550.1070.0000.0000.0000.0510.0000.0080.0000.0000.5431.0000.000
Loan_Status_Y0.0000.0530.0970.5340.0000.0780.0000.0410.0000.0710.0000.1270.0001.000

Missing values

2023-07-10T17:41:53.345304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-10T17:41:56.695652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryGender_MaleMarried_YesDependents_1Dependents_2Dependents_3+Education_Not GraduateSelf_Employed_YesProperty_Area_SemiurbanProperty_Area_UrbanLoan_Status_Y
05849.000.000146.412162360.01.01000000011
14583.001508.000128.000000360.01.01110000000
23000.000.00066.000000360.01.01100001011
32583.002358.000120.000000360.01.01100010011
46000.000.000141.000000360.01.01000000011
55417.004196.000261.500000360.01.01101001011
62333.001516.00095.000000360.01.01100010011
73036.002504.000158.000000360.00.01100100100
84006.001526.000168.000000360.01.01101000011
910171.255743.125261.500000360.01.01110000100
ApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryGender_MaleMarried_YesDependents_1Dependents_2Dependents_3+Education_Not GraduateSelf_Employed_YesProperty_Area_SemiurbanProperty_Area_UrbanLoan_Status_Y
60410171.250.0261.500000360.01.00110000101
6052400.003800.0146.412162360.01.01100010010
6063400.002500.0173.000000360.01.01110000101
6073987.001411.0157.000000360.01.01101010001
6083232.001950.0108.000000360.01.01100000001
6092900.000.071.000000360.01.00000000001
6104106.000.040.000000360.01.01100100001
6118072.00240.0253.000000360.01.01110000011
6127583.000.0187.000000360.01.01101000011
6134583.000.0133.000000360.00.00000001100

Duplicate rows

Most frequently occurring

ApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryGender_MaleMarried_YesDependents_1Dependents_2Dependents_3+Education_Not GraduateSelf_Employed_YesProperty_Area_SemiurbanProperty_Area_UrbanLoan_Status_Y# duplicates
010171.250.0261.5360.01.011001000012
2023-07-10T17:42:22.910203 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2023-07-10T17:42:47.831451 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/